Callejon-Leblic, Maria A. , MORENO LUNA, RAMÓN, Del Cuvillo, Alfonso , Reyes-Tejero, Isabel M. , Garcia-Villaran, Miguel A. , Santos-Pena, Marta , Maza-Solano, Juan M. , Martin-Jimenez, Daniel , Palacios-Garcia, Jose M. , Fernandez-Velez, Carlos , Gonzalez-Garcia, Jaime , Sanchez-Calvo, Juan M. , Solanellas-Soler, Juan , Sanchez-Gomez, Serafin
No
J. Clin. Med.
Article
Científica
4.964
1.04
01/02/2021
000623998200001
The COVID-19 outbreak has spread extensively around the world. Loss of smell and taste have emerged as main predictors for COVID-19. The objective of our study is to develop a comprehensive machine learning (ML) modelling framework to assess the predictive value of smell and taste disorders, along with other symptoms, in COVID-19 infection. A multicenter case-control study was performed, in which suspected cases for COVID-19, who were tested by real-time reverse-transcription polymerase chain reaction (RT-PCR), informed about the presence and severity of their symptoms using visual analog scales (VAS). ML algorithms were applied to the collected data to predict a COVID-19 diagnosis using a 50-fold cross-validation scheme by randomly splitting the patients in training (75%) and testing datasets (25%). A total of 777 patients were included. Loss of smell and taste were found to be the symptoms with higher odds ratios of 6.21 and 2.42 for COVID-19 positivity. The ML algorithms applied reached an average accuracy of 80%, a sensitivity of 82%, and a specificity of 78% when using VAS to predict a COVID-19 diagnosis. This study concludes that smell and taste disorders are accurate predictors, with ML algorithms constituting helpful tools for COVID-19 diagnostic prediction.
COVID-19; machine learning; prediction model; SARS-CoV-2; smell; taste; visual analog scale